Despite not being a time series model, many topic models feature a Dirichlet-sampled topic-word matrix. The Latent Dirichlet Allocation (LDA) [1]} is a prominent example. We could semantically order the vocabulary of words, for instance from negative to positive, or boring to funny words. Then, we may want the inferred latent topics to mirror this observed ordering. We can also imagine ordinal topic models beyond textual data such as ordinal topics over ranked athletes from different sports teams.
